Macroeconomic forecasting with mixed data sampling frequencies: Evidence from a small open economy

• Author: Albert K. Tsui,Cheng Yang Xu,Zhaoyong Zhang
• DOI: http://doi.org/10.1002/for.2528
• Published date: 01 September 2018
• Date: 01 September 2018

RESEARCH ARTICLE

Macroeconomic forecasting with mixed data sampling

frequencies: Evidence from a small open economy

Albert K. Tsui

1

| Cheng Yang Xu

1

| Zhaoyong Zhang

2

1

Department of Economics, National

University of Singapore, Singapore

2

School of Business and Law, Edith

Cowan University, Joondalup, WA,

Australia

Correspondence

Zhaoyong Zhang, School of Business and

Law, Edith Cowan University, 270

Joondalup Drive, Joondalup, WA 6027,

Australia.

Email: zhaoyong.zhang@ecu.edu.au

Abstract

The aim of this study was to forecast the Singapore gross domestic product

(GDP) growth rate by employing the mixed‐data sampling (MIDAS) approach

using mixed and high‐frequency financial market data from Singapore, and to

examine whether the high‐frequency financial variables could better predict

the macroeconomic variables. We adopt different time‐aggregating methods

to handle the high‐frequency data in order to match the sampling rate of

lower‐frequency data in our regression models. Our results showed that

MIDAS regression using high‐frequency stock return data produced a better

forecast of GDP growth rate than the other models, and the best forecasting

performance was achieved by using weekly stock returns. The forecasting

result was further improved by performing intra‐period forecasting.

KEYWORDS

financial variable, forecast evaluation,forecasting, mixed frequencies

1|INTRODUCTION

When forecasting macroeconomic variables such as gross

domestic product (GDP) growth rate, researchers often

face a dilemma because data are not all sampled at the

same frequency. Most macroeconomic data are sampled

monthly (e.g., inflation, employment) or quarterly (e.g.,

GDP), whereas most financial variables (e.g., interest

rates and asset prices) are sampled daily or even more fre-

quently. Furthermore, asset prices are forward looking,

and they are long believed to contain useful information

about future economic developments (Stock & Watson,

2003). It is therefore interesting to examine whether or

not one can use high‐frequency financial variables to bet-

ter estimate and forecast macroeconomic variables. This

is particularly useful for the central banks, financial

firms, and any other entity whose outcome depends on

business cycle conditions and who needs to monitor the

state of the economy in real time.

However, the main challenge is how best to use such

available data—especially the high‐frequency data—in

our economic forecast, and how to effectively conduct the

time‐aggregating, such as averaging, of the high‐frequency

data to match the sampling rate of lower‐frequency data,

as time aggregationalways leads to loss of individual timing

information that might be important for forecasting.

Conventional forecasting models generally require the data

to be of the same frequency. Time aggregating, such as

averaging, of the high‐frequency data is usually practiced

to match the sampling rate of lower‐frequency data. But

time aggregation always leads to loss of individual timing

information that might be important for forecasting. Using

mixed‐frequency data in forecasting is expected to outper-

form conventional forecasting models, which generally

require data with the same (low) frequency. Hence finding

a suitable method to handle the high‐frequency data is a

crucial task for every forecaster dealing with mixed‐fre-

quency data.

One promising method currently available is the

mixed‐data sampling (MIDAS) regression model first

introduced by Ghysels, Santa‐Clara, and Valkanov

(2004), Ghysels, Sinko, and Valkanov (2007), and

Received: 18 September 2017 Revised: 3 January 2018 Accepted: 2 April 2018

DOI: 10.1002/for.2528

666 Copyright © 2018 John Wiley & Sons, Ltd. Journal of Forecasting. 2018;37:666–675.wileyonlinelibrary.com/journal/for